TY - JOUR
T1 - Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model
AU - Abujazar, Mohammed Shadi S.
AU - Fatihah, Suja
AU - Ibrahim, Ibrahim Anwar
AU - Kabeel, A. E.
AU - Sharil, Suraya
PY - 2018/1/1
Y1 - 2018/1/1
N2 - This paper presents a cascaded forward neural network model for predicting the productivity of a developed inclined stepped solar still system. The actual recorded data of the developed inclined stepped solar still system is used to develop the proposed model. The results of the predicted productivity are compared with that obtained from regression and linear models. In this study, three statistical error terms are used to evaluate the proposed model: root mean square error (RMSE), mean absolute percentage error (MAPE) and mean bias error (MBE). The results show that the proposedcascaded forward neural network (CFNN) model more accurately predicts the productivity of the system than the other modelsmentioned. The RMSE, MAPE and MBE values of the proposed model are 22.48%, 18.51% and −26.46%, respectively. Therefore, the CFNN model provides benefits for modelling the solar still.
AB - This paper presents a cascaded forward neural network model for predicting the productivity of a developed inclined stepped solar still system. The actual recorded data of the developed inclined stepped solar still system is used to develop the proposed model. The results of the predicted productivity are compared with that obtained from regression and linear models. In this study, three statistical error terms are used to evaluate the proposed model: root mean square error (RMSE), mean absolute percentage error (MAPE) and mean bias error (MBE). The results show that the proposedcascaded forward neural network (CFNN) model more accurately predicts the productivity of the system than the other modelsmentioned. The RMSE, MAPE and MBE values of the proposed model are 22.48%, 18.51% and −26.46%, respectively. Therefore, the CFNN model provides benefits for modelling the solar still.
KW - ANN
KW - modelling
KW - performance evaluation
KW - prediction
KW - productivity
KW - solar desalination
KW - solar still
UR - http://www.scopus.com/inward/record.url?scp=85031932663&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2017.09.092
DO - 10.1016/j.jclepro.2017.09.092
M3 - Article
AN - SCOPUS:85031932663
SN - 0959-6526
VL - 170
SP - 147
EP - 159
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
ER -